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Cleanlab: Labeled Datasets that Correct Themselves Automatically

Posted Jun 30, 2022 | Views 252
# Cleanlab
# Labeled Datasets
# Data-centric AI
# Cleanlab.ai
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Curtis Northcutt
CEO & Co-Founder @ Cleanlab

Curtis Northcutt is the CEO and Co-Founder of Cleanlab focused on making AI work reliably for people and their messy, real-world data by automatically fixing issues in any ML dataset. Curtis completed his Ph.D. in Computer Science at MIT, receiving the MIT Thesis Award, NSF Fellowship, and the Goldwater Scholarship. Prior to Cleanlab, Curtis worked at AI research groups including Google, Oculus, Amazon, Facebook, Microsoft, and NASA.

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Demetrios Brinkmann
Chief Happiness Engineer @ MLOps Community

At the moment Demetrios is immersing himself in Machine Learning by interviewing experts from around the world in the weekly MLOps.community meetups. Demetrios is constantly learning and engaging in new activities to get uncomfortable and learn from his mistakes. He tries to bring creativity into every aspect of his life, whether that be analyzing the best paths forward, overcoming obstacles, or building lego houses with his daughter.

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Vishnu Rachakonda
Data Scientist @ Firsthand

Vishnu Rachakonda is the operations lead for the MLOps Community and co-hosts the MLOps Coffee Sessions podcast. He is a machine learning engineer at Tesseract Health, a 4Catalyzer company focused on retinal imaging. In this role, he builds machine learning models for clinical workflow augmentation and diagnostics in on-device and cloud use cases. Since studying bioengineering at Penn, Vishnu has been actively working in the fields of computational biomedicine and MLOps. In his spare time, Vishnu enjoys suspending all logic to watch Indian action movies, playing chess, and writing.

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SUMMARY

Pioneered at MIT by 3 Ph.D. Co-Founders, Cleanlab is an open-source/SaaS company building the premier data-centric AI tools workflows for (1) automatically correcting messy data and labels, (2) auto-tracking of dataset quality over time, (3) automatically finding classes to merge and delete, (4) auto ml for data tasks, (5) obtaining and ranking high-quality annotations, and (6) training ML models with messy data.

Most of the prescriptive tasks (finding issues) can be done in one line of code with their open-source product: https://github.com/cleanlab/cleanlab.

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